
<!DOCTYPE html>
    <html>
    <head>
        <meta charset="UTF-8">
        <title>Awesome Deep Reinforcement Learning</title>
        
        <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/Microsoft/vscode/extensions/markdown-language-features/media/markdown.css">
        <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/Microsoft/vscode/extensions/markdown-language-features/media/highlight.css">
        
        <style>
.task-list-item { list-style-type: none; } .task-list-item-checkbox { margin-left: -20px; vertical-align: middle; }
</style>
        <style>
            body {
                font-family: -apple-system, BlinkMacSystemFont, 'Segoe WPC', 'Segoe UI', 'Ubuntu', 'Droid Sans', sans-serif;
                font-size: 14px;
                line-height: 1.6;
            }
        </style>
        
        
    </head>
    <body class="vscode-light">
        <p>Awesome Deep Reinforcement Learning</p>
        
<h1 id="awesome-deep-reinforcement-learning">Awesome Deep Reinforcement Learning</h1>

<p><a href="https://github.com/tigerneil/awesome-deep-rl">Follow <b>awesome-deep-rl</b> on Github</a></p>
<p><a href="https://github.com/tigerneil/awesome-deep-rl/issues">Ask questions on Github</a></p>  

<p>updated Landscape of <strong>DRL</strong></p>
        
<p><img src="images/awesome-drl.png" alt="updated Landscape of DRL"></p>
        
<p>Landscape of <strong>DRL</strong></p>
        
<p><img src="images/landscape.jpeg" alt="Landscape of DRL/GAN"></p>
    
<p>This project is built for people who are learning and researching on latest deep reinforcement learning methods.</p>
        
<p>Illustrations:</p>
        
<p><img src="images/ACER.png" alt=""></p>
        
<p><strong>Recommendations and suggestions are welcome</strong>.</p>
        
<h2 id="general-guidances">General guidances</h2>
        
<ul>
<li><a href="http://mlanctot.info/files/papers/Lanctot_MARL_RLSS2019_Lille.pdf">Multiagent Reinforcement Learning by Marc Lanctot RLSS @ Lille</a> 11 July 2019</li>
<li><a href="https://david-abel.github.io/notes/rldm_2019.pdf">RLDM 2019 Notes by David Abel</a> 11 July 2019</li>
<li><a href="RLNL.md">A Survey of Reinforcement Learning Informed by Natural Language</a> 10 Jun 2019 <a href="https://arxiv.org/pdf/1906.03926.pdf">arxiv</a></li>
<li><a href="ChallengesRealWorldRL.md">Challenges of Real-World Reinforcement Learning</a> 29 Apr 2019 <a href="https://arxiv.org/pdf/1904.12901.pdf">arxiv</a></li>
<li><a href="RayInterference.md">Ray Interference: a Source of Plateaus in Deep Reinforcement Learning</a> 25 Apr 2019 <a href="https://arxiv.org/pdf/1904.11455.pdf">arxiv</a></li>
<li><a href="p10.md">Principles of Deep RL by David Silver</a></li>
<li><a href="https://www.jianshu.com/p/dfd987aa765a">University AI's General introduction to deep rl (in Chinese)</a></li>
<li><a href="https://spinningup.openai.com/en/latest/">OpenAI's spinningup</a></li>
<li><a href="https://thegradient.pub/the-promise-of-hierarchical-reinforcement-learning/">The Promise of Hierarchical Reinforcement Learning</a> 9 Mar 2019</li>
<li><a href="reproducing.md">Deep Reinforcement Learning that Matters</a> 30 Jan 2019 <a href="https://arxiv.org/pdf/1709.06560.pdf">arxiv</a></li>
</ul>
<h2 id="foundations-and-theory">Foundations and theory</h2>
<ul>
<li><a href="GNLBE.md">General non-linear Bellman equations</a> 9 July 2019 <a href="https://arxiv.org/pdf/1907.07331.pdf">arxiv</a></li>
<li><a href="MCGE.md">Monte Carlo Gradient Estimation in Machine Learning</a> 25 Jun 2019 <a href="https://arxiv.org/pdf/1906.10652.pdf">arxiv</a></li>
</ul>
<h2 id="general-benchmark-testing-frameworks">General Benchmark Testing Frameworks</h2>
<ul>
<li><a href="https://github.com/chainer/chainerrl-visualizer">Chainerrl Visualizer</a></li>
<li><a href="BSRL.md">Behaviour Suite for Reinforcement Learning</a> 13 Aug 2019 <a href="https://arxiv.org/pdf/1908.03568.pdf">arxiv</a> | <a href="https://github.com/deepmind/bsuite">code</a></li>
<li><a href="Coinrun.md">Quantifying Generalization in Reinforcement Learning</a> 20 Dec 2018 <a href="https://arxiv.org/pdf/1812.02341.pdf">arxiv</a></li>
<li><a href="SRL.md">S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning</a> 25 Sept 2018</li>
<li><a href="https://github.com/google/dopamine">dopamine</a></li>
<li><a href="https://github.com/deepmind/pysc2">StarCraft II</a></li>
<li><a href="https://github.com/deepmind/trfl">tfrl</a></li>
<li><a href="https://github.com/chainer/chainerrl">chainerrl</a></li>
<li><a href="https://github.com/PaddlePaddle/PARL">PARL</a></li>
</ul>
<h2 id="value-based-methods">Value based methods</h2>
<ul>
<li><a href="RVF.md">Recurrent Value Functions</a> 23 May 2019 <a href="https://arxiv.org/pdf/1905.09562.pdf">arxiv</a></li>
<li><a href="LipschitzQ.md">Stochastic Lipschitz Q-Learning</a> 24 Apr 2019 <a href="https://arxiv.org/pdf/1904.10653.pdf">arxiv</a></li>
<li><a href="https://arxiv.org/pdf/1710.11417">TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning</a> 8 Mar 2018</li>
<li><a href="https://arxiv.org/pdf/1803.00933.pdf">DISTRIBUTED PRIORITIZED EXPERIENCE REPLAY</a> 2 Mar 2018</li>
<li><a href="Rainbow.md">Rainbow: Combining Improvements in Deep Reinforcement Learning</a> 6 Oct 2017</li>
<li><a href="DQfD.md">Learning from Demonstrations for Real World Reinforcement Learning</a> 12 Apr 2017</li>
<li><a href="Dueling.md">Dueling Network Architecture</a></li>
<li><a href="DDQN.md">Double DQN</a></li>
<li><a href="PER.md">Prioritized Experience</a></li>
<li><a href="DQN.md">Deep Q-Networks</a></li>
</ul>
<h2 id="policy-gradient-methods">Policy gradient methods</h2>
<ul>
<li><a href="DirPG.md">Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces</a> <a href="https://arxiv.org/pdf/1906.06062.pdf">arxiv</a></li>
<li><a href="PGS.md">Policy Gradient Search: Online Planning and Expert Iteration without Search Trees</a> 7 Apr 2019 <a href="https://arxiv.org/pdf/1904.03646.pdf">arxiv</a></li>
<li><a href="SPU.md">SUPERVISED POLICY UPDATE FOR DEEP REINFORCEMENT LEARNING</a> 24 Dec 2018 <a href="https://arxiv.org/pdf/1805.11706v4.pdf">arxiv</a></li>
<li><a href="PPO-CMA.md">PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation</a> 5 Oct 2018 <a href="https://arxiv.org/pdf/1810.02541v6.pdf">arxiv</a></li>
<li><a href="CAPG.md">Clipped Action Policy Gradient</a> 22 June 2018</li>
<li><a href="EPG.md">Expected Policy Gradients for Reinforcement Learning</a> 10 Jan 2018</li>
<li><a href="PPO.md">Proximal Policy Optimization Algorithms</a> 20 July 2017</li>
<li><a href="DPPO.md">Emergence of Locomotion Behaviours in Rich Environments</a> 7 July 2017</li>
<li><a href="IPG.md">Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning</a> 1 Jun 2017</li>
<li><a href="PGSQL.md">Equivalence Between Policy Gradients and Soft Q-Learning</a></li>
<li><a href="TRPO.md">Trust Region Policy Optimization</a></li>
<li><a href="DEBP.md">Reinforcement Learning with Deep Energy-Based Policies</a></li>
<li><a href="QPROP.md">Q-PROP: SAMPLE-EFFICIENT POLICY GRADIENT WITH AN OFF-POLICY CRITIC</a></li>
</ul>
<h2 id="explorations-in-drl">Explorations in DRL</h2>
<ul>
<li><a href="Disagreement.md">Self-Supervised Exploration via Disagreement</a> 10 Jun 2019 <a href="https://arxiv.org/pdf/1906.04161.pdf">arxiv</a></li>
<li><a href="MBIE-EB.md">Approximate Exploration through State Abstraction</a> 24 Jan 2019</li>
<li><a href="UBE.md">The Uncertainty Bellman Equation and Exploration</a> 15 Sep 2017</li>
<li><a href="NoisyNet.md">Noisy Networks for Exploration</a> 30 Jun 2017 <a href="https://github.com/Kaixhin/NoisyNet-A3C">implementation</a></li>
<li><a href="PhiEB.md">Count-Based Exploration in Feature Space for Reinforcement Learning</a> 25 Jun 2017</li>
<li><a href="NDM.md">Count-Based Exploration with Neural Density Models</a> 14 Jun 2017</li>
<li><a href="QEnsemble.md">UCB and InfoGain Exploration via Q-Ensembles</a> 11 Jun 2017</li>
<li><a href="MMRB.md">Minimax Regret Bounds for Reinforcement Learning</a> 16 Mar 2017</li>
<li><a href="incentivizing.md">Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models</a></li>
<li><a href="EX2.md">EX2: Exploration with Exemplar Models for Deep Reinforcement Learning</a></li>
</ul>
<h2 id="actor-critic-methods">Actor-Critic methods</h2>
<ul>
<li><a href="Geoff-PAC.md">Generalized Off-Policy Actor-Critic</a> 27 Mar 2019</li>
<li><a href="https://arxiv.org/pdf/1812.05905.pdf">Soft Actor-Critic Algorithms and Applications</a> 29 Jan 2019</li>
<li><a href="REACTOR.md">The Reactor: A Sample-Efficient Actor-Critic Architecture</a> 15 Apr 2017</li>
<li><a href="ACER.md">SAMPLE EFFICIENT ACTOR-CRITIC WITH EXPERIENCE REPLAY</a></li>
<li><a href="UNREAL.md">REINFORCEMENT LEARNING WITH UNSUPERVISED AUXILIARY TASKS</a></li>
<li><a href="DDPG.md">Continuous control with deep reinforcement learning</a></li>
</ul>
<h2 id="model-based-methods">Model-based methods</h2>
<ul>
<li><a href="parametric.md">When to use parametric models in reinforcement learning?</a> 12 Jun 2019 <a href="https://arxiv.org/pdf/1906.05243.pdf">arxiv</a></li>
<li><a href="https://arxiv.org/pdf/1903.00374.pdf">Model Based Reinforcement Learning for Atari</a> 5 Mar 2019</li>
<li><a href="MBDQN.md">Model-Based Stabilisation of Deep Reinforcement Learning</a> 6 Sep 2018</li>
<li><a href="IBP.md">Learning model-based planning from scratch</a> 19 July 2017</li>
</ul>
<h2 id="model-free--model-based">Model-free + Model-based</h2>
<ul>
<li><a href="I2As.md">Imagination-Augmented Agents for Deep Reinforcement Learning</a> 19 July 2017</li>
</ul>
<h2 id="hierarchical">Hierarchical</h2>
<ul>
<li><a href="HIRO.md">WHY DOES HIERARCHY (SOMETIMES) WORK SO WELL IN REINFORCEMENT LEARNING?</a> 23 Sep 2019 <a href="https://arxiv.org/pdf/1909.10618.pdf">arxiv</a></li>
<li><a href="HAL.md">Language as an Abstraction for Hierarchical Deep Reinforcement Learning</a> 18 Jun 2019 <a href="https://arxiv.org/pdf/1906.07343.pdf">arxiv</a></li>
</ul>
<h2 id="option">Option</h2>
<ul>
<li><a href="VALOR.md">Variational Option Discovery Algorithms</a> 26 July 2018</li>
<li><a href="LFOD.md">A Laplacian Framework for Option Discovery in Reinforcement Learning</a> 16 Jun 2017</li>
</ul>
<h2 id="connection-with-other-methods">Connection with other methods</h2>
<ul>
<li><a href="GVG.md">Robust Imitation of Diverse Behaviors</a></li>
<li><a href="GAIL.md">Learning human behaviors from motion capture by adversarial imitation</a></li>
<li><a href="GANAC.md">Connecting Generative Adversarial Networks and Actor-Critic Methods</a></li>
</ul>
<h2 id="connecting-value-and-policy-methods">Connecting value and policy methods</h2>
<ul>
<li><a href="PCL.md">Bridging the Gap Between Value and Policy Based Reinforcement Learning</a></li>
<li><a href="PGQ.md">Policy gradient and Q-learning</a></li>
</ul>
<h2 id="reward-design">Reward design</h2>
<ul>
<li><a href="VICE.md">End-to-End Robotic Reinforcement Learning without Reward Engineering</a> 16 Apr 2019 <a href="https://arxiv.org/pdf/1904.07854.pdf">arxiv</a></li>
<li><a href="RLCRC.md">Reinforcement Learning with Corrupted Reward Channel</a> 23 May 2017</li>
</ul>
<h2 id="unifying">Unifying</h2>
<ul>
<li><a href="MSRL.md">Multi-step Reinforcement Learning: A Unifying Algorithm</a></li>
</ul>
<h2 id="faster-drl">Faster DRL</h2>
<ul>
<li><a href="NEC.md">Neural Episodic Control</a></li>
</ul>
<h2 id="apply-rl-to-other-domains">Apply RL to other domains</h2>
<ul>
<li><a href="RLTUNER.md">TUNING RECURRENT NEURAL NETWORKS WITH REINFORCEMENT LEARNING</a></li>
</ul>
<h2 id="multiagent-settings">Multiagent Settings</h2>
<ul>
<li><a href="Dip.md">No Press Diplomacy: Modeling Multi-Agent Gameplay</a> 4 Sep 2019 <a href="https://arxiv.org/pdf/1909.02128.pdf">arxiv</a></li>
<li><a href="OPRE">Options as responses: Grounding behavioural hierarchies in multi-agent RL</a> 6 Jun 2019 <a href="https://arxiv.org/pdf/1906.01470.pdf">arxiv</a></li>
<li><a href="MERL.md">Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination</a> 18 Jun 2019 <a href="https://arxiv.org/pdf/1906.07315.pdf">arxiv</a></li>
<li><a href="ROMMEO.md">A Regularized Opponent Model with Maximum Entropy Objective</a> 17 May 2019 <a href="https://arxiv.org/pdf/1905.08087.pdf">arxiv</a></li>
<li><a href="NashDQN.md">Deep Q-Learning for Nash Equilibria: Nash-DQN</a> 23 Apr 2019 <a href="https://arxiv.org/pdf/1904.10554.pdf">arxiv</a></li>
<li><a href="MRL.md">Malthusian Reinforcement Learning</a> 3 Mar 2019 <a href="https://arxiv.org/pdf/1812.07019.pdf">arxiv</a></li>
<li><a href="bad.md">Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning</a> 4 Nov 2018</li>
<li><a href="ISMCI.md">INTRINSIC SOCIAL MOTIVATION VIA CAUSAL INFLUENCE IN MULTI-AGENT RL</a> 19 Oct 2018</li>
<li><a href="http://www.cs.ox.ac.uk/people/shimon.whiteson/pubs/rashidicml18.pdf">QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning</a> 30 Mar 2018</li>
<li><a href="SOM.md">Modeling Others using Oneself in Multi-Agent Reinforcement Learning</a> 26 Feb 2018</li>
<li><a href="SGA.md">The Mechanics of n-Player Differentiable Games</a> 15 Feb 2018</li>
<li><a href="RoboSumo.md">Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments</a> 10 Oct 2017</li>
<li><a href="LOLA.md">Learning with Opponent-Learning Awareness</a> 13 Sep 2017</li>
<li><a href="COMA.md">Counterfactual Multi-Agent Policy Gradients</a></li>
<li><a href="MADDPG.md">Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments</a> 7 Jun 2017</li>
<li><a href="BiCNet.md">Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games</a> 29 Mar 2017</li>
</ul>
<h2 id="new-design">New design</h2>
<ul>
<li><a href="https://arxiv.org/pdf/1802.01561.pdf">IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures</a> 9 Feb 2018</li>
<li><a href="RECUR.md">Reverse Curriculum Generation for Reinforcement Learning</a></li>
<li><a href="HIRL.md">Trial without Error: Towards Safe Reinforcement Learning via Human Intervention</a></li>
<li><a href="DualMDP.md">Learning to Design Games: Strategic Environments in Deep Reinforcement Learning</a> 5 July 2017</li>
</ul>
<h2 id="multitask">Multitask</h2>
<ul>
<li><a href="https://arxiv.org/pdf/1803.03835.pdf">Kickstarting Deep Reinforcement Learning</a> 10 Mar 2018</li>
<li><a href="ZSTG.md">Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning</a> 7 Nov 2017</li>
<li><a href="Distral.md">Distral: Robust Multitask Reinforcement Learning</a> 13 July 2017</li>
</ul>
<h2 id="observational-learning">Observational Learning</h2>
<ul>
<li><a href="OLRL.md">Observational Learning by Reinforcement Learning</a> 20 Jun 2017</li>
</ul>
<h2 id="meta-learning">Meta Learning</h2>
<ul>
<li><a href="GVF.md">Discovery of Useful Questions as Auxiliary Tasks</a> 10 Sep 2019 <a href="https://arxiv.org/pdf/1909.04607.pdf">arxiv</a></li>
<li><a href="MetaSS.md">Meta-learning of Sequential Strategies</a> 8 May 2019 <a href="https://arxiv.org/pdf/1905.03030.pdf">arxiv</a></li>
<li><a href="PEARL.md">Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables</a> 19 Mar 2019 <a href="https://arxiv.org/pdf/1903.08254.pdf">arxiv</a></li>
<li><a href="E2.md">Some Considerations on Learning to Explore via Meta-Reinforcement Learning</a> 11 Jan 2019 <a href="https://arxiv.org/pdf/1803.01118.pdf">arxiv</a></li>
<li><a href="MGRL.md">Meta-Gradient Reinforcement Learning</a> 24 May 2018 <a href="https://arxiv.org/pdf/1805.09801.pdf">arxiv</a></li>
<li><a href="ProMP.md">ProMP: Proximal Meta-Policy Search</a> 16 Oct 2018 <a href="https://arxiv.org/pdf/1810.06784">arxiv</a></li>
<li><a href="UML.md">Unsupervised Meta-Learning for Reinforcement Learning</a> 12 Jun 2018</li>
</ul>
<h2 id="distributional">Distributional</h2>
<ul>
<li><a href="GANQL.md">GAN Q-learning</a> 20 July 2018</li>
<li><a href="IQN.md">Implicit Quantile Networks for Distributional Reinforcement Learning</a> 14 Jun 2018</li>
<li><a href="GTD.md">Nonlinear Distributional Gradient Temporal-Difference Learning</a> 20 May 2018</li>
<li><a href="D4PG.md">DISTRIBUTED DISTRIBUTIONAL DETERMINISTIC POLICY GRADIENTS</a> 23 Apr 2018</li>
<li><a href="C51-analysis.md">An Analysis of Categorical Distributional Reinforcement Learning</a> 22 Feb 2018</li>
<li><a href="QR-DQN.md">Distributional Reinforcement Learning with Quantile Regression</a> 27 Oct 2017</li>
<li><a href="C51.md">A Distributional Perspective on Reinforcement Learning</a> 21 July 2017</li>
</ul>
<h2 id="planning">Planning</h2>
<ul>
<li><a href="SoRB.md">Search on the Replay Buffer: Bridging Planning and Reinforcement Learning</a> 12 June 2019 <a href="https://arxiv.org/pdf/1906.05253.pdf">arxiv</a></li>
</ul>
<h2 id="safety">Safety</h2>
<ul>
<li><a href="MPO.md">Robust Reinforcement Learning for Continuous Control with Model Misspecification</a> 18 Jun 2019 <a href="https://arxiv.org/pdf/1906.07516.pdf">arxiv</a></li>
<li><a href="Viper.md">Verifiable Reinforcement Learning via Policy Extraction</a> 22 May 2018 <a href="https://arxiv.org/pdf/1805.08328.pdf">arxiv</a></li>
</ul>
<h2 id="inverse-rl">Inverse RL</h2>
<ul>
<li><a href="OP-GAIL.md">ADDRESSING SAMPLE INEFFICIENCY AND REWARD BIAS IN INVERSE REINFORCEMENT LEARNING</a> 9 Sep 2018</li>
</ul>
<h2 id="no-reward-rl">No reward RL</h2>
<ul>
<li><a href="VISR.md">Fast Task Inference with Variational Intrinsic Successor Features</a> 2 Jun 2019 <a href="https://arxiv.org/pdf/1906.05030.pdf">arxiv</a></li>
<li><a href="https://arxiv.org/pdf/1705.05363">Curiosity-driven Exploration by Self-supervised Prediction</a> 15 May 2017</li>
</ul>
<h2 id="time">Time</h2>
<ul>
<li><a href="Intervaltime.md">Interval timing in deep reinforcement learning agents</a> 31 May 2019 <a href="https://arxiv.org/pdf/1905.13469.pdf">arxiv</a></li>
<li><a href="PEB.md">Time Limits in Reinforcement Learning</a></li>
</ul>
<h2 id="applications">Applications</h2>
<ul>
<li><a href="dmimic.md">DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills</a> 9 Apr 2018</li>
</ul>
<h2 id="adversarial-learning">Adversarial learning</h2>
<ul>
<li><a href="LQR+GAIfO.md">Sample-efficient Adversarial Imitation Learning from Observation</a> 18 Jun 2019 <a href="https://arxiv.org/pdf/1906.07374.pdf">arxiv</a></li>
</ul>
<h2 id="use-natural-language">Use Natural Language</h2>
<ul>
<li><a href="LEARN.md">Using Natural Language for Reward Shaping in Reinforcement Learning</a> 31 May 2019 <a href="https://www.cs.utexas.edu/~ai-lab/downloadPublication.php?filename=http://www.cs.utexas.edu/users/ml/papers/goyal.ijcai19.pdf&amp;pubid=127757">arxiv</a></li>
</ul>
<h2 id="generative-and-contrastive-representation-learning">Generative and contrastive representation learning</h2>
<ul>
<li><a href="ST-DIM.md">Unsupervised State Representation Learning in Atari</a> 19 Jun 2019 <a href="https://arxiv.org/pdf/1906.08226.pdf">arxiv</a></li>
</ul>
<h2 id="belief">Belief</h2>
<ul>
<li><a href="GenerativeBelief.md">Shaping Belief States with Generative Environment Models for RL</a> 24 Jun 2019 <a href="https://arxiv.org/pdf/1906.09237v2.pdf">arxiv</a></li>
</ul>
<h2 id="pac">PAC</h2>
<ul>
<li><a href="COF-PAC.md">Provably Convergent Off-Policy Actor-Critic with Function Approximation</a> 11 Nov 2019 <a href="https://arxiv.org/pdf/1911.04384.pdf">arxiv</a></li>
</ul>
<h2 id=""></h2>
<ul>
<li><a href="Reciprocity.md">Learning Reciprocity in Complex Sequential Social Dilemmas</a> 19 Mar 2019 <a href="https://arxiv.org/pdf/1903.08082.pdf">arxiv</a></li>
</ul>
<script type="text/javascript" id="clustrmaps" src="//clustrmaps.com/map_v2.js?d=8WVXR3vTf3a1tXQExUQWRGVpVaABvZvooyRkmM4_0XQ&cl=ffffff&w=a"></script>    </body>
    </html>
